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Found 7 papers, 1 papers with code
SCL-VI: Self-supervised Context Learning for Visual Inspection of Industrial Defects
Current unsupervised models struggle to strike a balance between detecting texture and object defects, lacking the capacity to discern latent representations and intricate features.
Visual Anomaly Detection via Dual-Attention Transformer and Discriminative Flow
In this paper, we introduce the novel state-of-the-art Dual-attention Transformer and Discriminative Flow (DADF) framework for visual anomaly detection.
Ranked #14 on
Anomaly Detection
on MVTec LOCO AD
Learning Global-Local Correspondence with Semantic Bottleneck for Logical Anomaly Detection
To address this issue, we propose a two-branch approach that consists of a local branch for detecting structural anomalies and a global branch for detecting logical anomalies.
Ranked #16 on
Anomaly Detection
on MVTec LOCO AD
Generalizable Industrial Visual Anomaly Detection with Self-Induction Vision Transformer
To tackle the above limitations, we proposed a self-induction vision Transformer(SIVT) for unsupervised generalizable multi-category industrial visual anomaly detection and localization.
Normal Reference Attention and Defective Feature Perception Network for Surface Defect Detection
Unlike most reconstruction-based methods, our NDP-Net first employs an encoding module that extracts multi scale discriminative features of the surface textures, which is augmented with the defect discriminative ability by the proposed artificial defects and the novel pixel-level defect perception loss.
Siamese Transition Masked Autoencoders as Uniform Unsupervised Visual Anomaly Detector
The extensive experiments conducted demonstrate that the proposed ST-MAE method can advance state-of-the-art performance on multiple benchmarks across application scenarios with a superior inference efficiency, which exhibits great potential to be the uniform model for unsupervised visual anomaly detection.
A Feature Memory Rearrangement Network for Visual Inspection of Textured Surface Defects Toward Edge Intelligent Manufacturing
Subsequently, a contrastive-learning-based memory feature module (CMFM) is proposed to obtain discriminative representations and construct a normal feature memory bank in the latent space, which can be employed as a substitute for defects and fast anomaly scores at the patch level.
